member of the GPS-III Independent Review Team and
Bibliographic record
Abstract
Scientific Advisory Board for the USAF and serves on the GPS World editorial advisory board. Neil Gerein is a Product Manager for NAVSYS Corporation’s Receivers Group and is responsible for the management and development of NAVSYS ’ next generation of GPS receivers. He is currently completing his M.Sc. in Electrical Engineering and holds a BSEE in Electrical Engineering from the University of Saskatchewan. NAVSYS has developed an Advanced GPS Hybrid Simulator (AGHS) architecture to address next generation GPS testing issues for the civilian and military markets. The AGHS is a hybrid software, digital and radio frequency (RF) GPS simulator design. In addition to providing digital and RF signal simulation capability, the AGHS can also be used to record and play-back realworld GPS signals from field tests. This paper describes the modular AGHS architecture and its various uses. Digitally created simulation files were created with the system and played back into the NAVSYS Advanced GPS Receiver (AGR). Test results are included that compare the digitally created files to their real-world counterparts showing the precision that can be achieved with the AGHS digital simulation approach. Test data collected during jammer tests
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".